"Post-Doc Research Associate"

Full-time post-doctoral position at the University of North Carolina at Chapel Hill in the Lineberger Comprehensive Cancer Center. The position is part of UNC's new NIH/FDA-funded Center for Tobacco Regulatory Science and Health Equity. We are looking for candidates interested in developing expertise in tobacco control policy research.

The broad theme of the UNC Center is building the science for effective FDA regulation of and communication about tobacco products disproportionately used by priority populations. The specific project the postdoc will work on uses systems science methods including causal loop diagramming to integrate evidence and expert insights about factors that impact the effectiveness of a flavored cigar ban and simulation modeling to estimate the public health impact of such a ban in a variety of plausible post-ban scenarios among priority populations and the overall US population.

This mixed-methods project includes use of nationally representative surveys, literature reviews, causal loop diagramming, an expert elicitation process, and simulation modeling. The postdoc will take part in study design, implementation, data analysis, and interpretation and publication of study findings. The postdoc will also develop their own research questions regarding tobacco regulatory science and will receive multidisciplinary training and mentorship from senior researchers.

Along with other tobacco control trainees at UNC, postdocs will be able to participate in a journal club, a speaker series, and various webinars. In addition, travel funds will be available to present research.

Initial appointment is for one year with additional one-year appointments for up to a total of 3 years.

Excellent written and verbal communication. Experience with writing and publishing scientific research papers. Experience with qualitative methods or quantitative research methods, including statistical analyses and stakeholder engagement. Experience with systems science methods such as simulation modeling is preferred.